Neuroticism and processing of negative emotional faces:
A neuropsychological study
BY
Laura Orlandi
A thesis submitted in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE IN CLINICAL PSYCHOLOGY
at the
UNIVERSITEIT VAN AMSTERDAM
2017
Abstract
Facial expressions are of considerable importance for human beings, given their ability to convey valuable information. In particular, negative expressions of emotion, which may signal threat or danger, are quickly appraised and processed by the human brain. Nevertheless, there are differences in the way individuals process these emotional stimuli. These differences may be a function of various personality traits, such as neuroticism. In this study, we applied the paradigm developed by Hariri et al. (2000), in which participants had to match emotional faces, either fearful or angry, and scrambled faces, in order to investigate whether neuroticism moderates the activation of brain regions associated with processing of facial emotions. Moreover, we examined whether there are differences in behavioral responses as a function of neuroticism by determining the association between neuroticism scores and speed and/or accuracy in matching the emotional faces. We found that activation of certain brain regions during the face-matching task was
correlated with neuroticism, but that these correlations were not significant. Moreover, no behavioral effect of neuroticism was found. Interestingly, participants reacted differently to angry v. fearful faces, regardless their level of neuroticism. The data are consistent with previous research suggesting that fear and anger are two different kind of threatful stimuli that lead to different responses. The results provide weak evidence for an effect of neuroticism on neural activation as a response to negative facial expressions.
In everyday life, we are continuously surrounded by a huge variety of emotional stimuli, such as emotional words and film scenes. Relevant emotional stimuli to which humans are continuously exposed to are facial expressions. Being humans inherently social animals, facial expressions are particularly important because they convey valuable information (Blair, 2003). In particular, negative facial expressions, such as fear and anger, are critical because they can be considered as threatening stimuli that need to be promptly and properly appraised by the human brain. There is evidence that facial expressions of threat are detected faster than happy faces (Hansen & Hansen, 1988; Fox et al., 2000).
Hariri et al. (2000) developed an experimental paradigm for studying the processing of emotional faces of fear and anger. This paradigm is characterized by an experimental match condition in which participants view a target face showing either fear or anger and match this to one of two faces displayed below, and a sensorimotor control condition in which participants view a target geometrical form and match this to one of two geometrical forms displayed below. This paradigm has been applied in different neuropsychological studies investigating the neural basis of the processing of negative emotional faces. These studies have found a consistent pattern of strong bilateral amygdala activation in the experimental match condition in
comparison to the control condition (Hariri, Bookheimer, & Mazziotta, 2000; Hariri, Tessitore, Mattay, Fera, & Weinberg, 2002; Hariri et al., 2002; Ting Wang, Dapretto, Hariri, Sigman, & Bookheimer, 2004). These results are also consistent with a large amount of studies using different stimuli that has found an increased activation of the amygdala in response to fearful faces (Blair, Morris, Frith, Perret, & Dolan, 1999; Fusar-Poli, et al., 2009; Philips et al., 1998, 2001; Vytal & Hamann, 2010). It seems therefore that emotional responses have a clear neural substrate.
However, emotional stimuli can lead to different responses across individuals, as shown by their behaviour. For instance, some individuals have strong emotional reactions, such as crying, as they watch a sad film, while others don’t express any particular emotion. Multiple factors can
contribute to this variety of responses, such as genotype, sex, and personality characteristics. These differences can provide further understanding of the neural underpinnings of emotion processing. Recent neurobiological studies of emotions have adopted an individual differences approach, taking into account differences in personality traits and how these may modulate neural responses when exposed to emotional stimuli (Hamman & Canli, 2004). If in the past individual differences often were treated as statistical noise within experimental psychology, nowadays researchers consider them to be relevant. Already in the 70’s, Underwood (1975) acknowledged that individual differences are fundamental in the construction of a theory. These differences may underlie different cognitive and behavioural processes. As Plomin and Kosslyn (2001) posit, the universal perspective and the individual-differences perspective can be seen as complementary: They ask different questions and lead to different answers.
Individual differences in personality have been extensively studied and this work has resulted in several taxonomies of personality variables. A popular classification is the Big Five model, that groups descriptors of personality within five broad domains: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness (Costa & McCrae, 1992).
Neuroticism is characterized by negative affect, dissatisfaction, impulsivity, anxiety, irritation and anger (Canli et al., 2001; Costa & McCrae, 1980; Costa & McCrae, 1992) and it has been the focus of research as being a robust predictor of different psychological disorders (Kendler, Kuhn, & Prescott, 2004; Kotov, Gamez, Schmidt, & Watson, 2010; Ormel, Rosmalen, & Farmer, 2004).
Studies of the neurobiological substrates of emotion have considered the moderating function of neuroticism. For example, it has been demonstrated that highly neurotic individuals show stronger amygdala activation to negative than to positive stimuli (Canli et al., 2001). Moreover, a number of studies have tried to map the neurobiological underpinnings of
regions, such as ACC, medial PFC, insula, hippocampus, fusiform gyrus and middle temporal gyrus (Ormel et al, 2013; Canli, 2004; Chan et al., 2009).
Individuals differ in reaction to emotional stimuli not only in brain activation but also in their behavioural response. When exposed to facial expressions, individuals rapidly and quite effortlessly interpret the emotional expression of the other (Pantic and Rothkrantz, 2000). Several studies suggest that healthy individuals scoring high on neuroticism have altered
processing of emotional faces. One cognitive theory states that individuals with high trait anxiety, a construct related to neuroticism, tend to show a bias towards emotionally negative stimuli (Mathews and MacLoad, 1994, 2005). They selectively pay more attention to stimuli carrying negative emotional information. They also show a negative interpretation bias. This means that individuals high in neuroticism have the tendency to interpret ambiguous information in a negative way, which can further lead to anxiety or depressive symptoms. However, findings from previous studies are somewhat inconsistent. Some studies have found that neuroticism scores were negatively correlated with speed and/or accuracy in recognizing happy facial expressions, but no correlation was found with recognition of negative facial expressions (Andric et al., 2010; Chan, Goodwin, & Harmer, 2007). In contrast, other studies have found that healthy individuals with high scores on neuroticism or trait anxiety, were more sensitive in detecting fearful faces than individuals with low scores (Doty et al., 2013; Japee et al., 2009; Ladoucer et al., 2009). These inconsistencies may reflect methodological differences across the studies. These studies applied a variety of different methods, such as Visual search task, Emotional Face N-back and
recognition tasks, making it difficult to compare the results. Furthermore, as some researchers point out (Andric et al., 2010; Doty et al., 2013), many behavioural studies investigating the association between neuroticism and processing of emotional faces have adopted the so-called Extreme Groups Approach, investigating only individuals with the highest and lowest scores on neuroticism. This has limited the generalizability of the results of these studies.
The current study adopts an individual-differences approach and aims to investigate whether neuroticism is related to the neural and behavioural response to negative emotional faces. To our knowledge no study has applied the Hariri et al. (2000) paradigm to analyse how neural activation in response to fearful and angry faces is related to neuroticism. Using the Harriri et al. (2000) paradigm, we will perform a correlation analysis to test whether neural responses to negative emotional faces are affected by individual differences in neuroticism in a large sample of healthy individuals. Moreover, we will explore the relationship between
neuroticism and ability in matching negative emotional faces by calculating the correlation between neuroticism scores and speed and accuracy of matching the faces. If the correlation between neuroticism and speed/accuracy of matching emotional faces is significant, we will then perform a mediation analysis in order to investigate whether this association is mediated by the activation of specific brain areas involved in the processing of negative emotional faces.
METHOD Subjects
The employed sample consisted of 248 participants from the larger ‘Population Imaging
Study ’15-16’ which had as aim to investigate the relationship between brain activity and behavior in a large sample of healthy individuals. The behavioral tasks were provided by the Vrij
Universiteit (Amsterdam) and the University of Amsterdam (UvA). During the fMRI scanning, heart rate and breathing were also measured. Moreover, eye tracking was measured during the resting state. Participants had to be between 18 and 26 of age and were recruited from the University of Amsterdam and the Hogeschool van Amsterdam. We obtained complete MRI data for 217 participants (44.3% males). For the rest of the participants we did not have complete data because of artifacts. The final sample had a mean age of 22.16 years (SD = 1.92).
Information about age was missing for seven participants. All participants underwent a screening procedure, first by telephone and then at the laboratory, to be allowed into the scanner. All
participants provided written inform consent and received 50 euros for their participation.
Experimental paradigm
For this study, we applied the paradigm developed by Hariri et al. (2000). During this task
participants were briefly presented with a target face and two probe faces. They were instructed to decide which of the probe faces expressed the same emotion as the target face by pressing a button on one of two button boxes. The experiment consisted of four experimental blocks, alternated with four control blocks. An experimental block consisted of six consecutive trials, each lasting 5 seconds, in which the target face expressed either anger of fear (Fig. 1a). The emotional facial expressions were derived from the NimStim Face Stimulus Set (Research Network on Early Experience and Brain Development, Tottenham et al., 2009). The Network is currently analysing the validity of the stimuli. Research so far shows high agreement ratings amongst children and adults in judging the emotions displayed by the stimulus faces.In control blocks participants were presented with scrambled faces in the form of an oval, and they were instructed to match the orientation, either horizontal or vertical, of the probe ovals to the target oval (Fig. 1b). This task lasted 7 minutes. Accuracy and reaction time were recorded.
(a) (b)
Fig 1. Visual stimuli employed in the experimental paradigm. (a) Emotional faces. Subjects were instructed to select one of the two probe faces (bottom) expressing the same emotion as the
target face (top). (b) Scrambled faces. Subjects were instructed to select one of the ovals (bottom) that matches the orientation of the target oval (top).
Image acquisition and preprocessing
MRI imaging was performed using a 3T MRI scanner (Philips 3T Achieva), located at the Spinoza Centre for Neuroimaging (Amsterdam). We immobilized the participant’s head using foam pads to reduce motion artifacts. Furthermore, earplugs were used to moderate scanner noise.Functional and structural data were preprocessed and analyzed using FSL 5.0 (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012) and MATLAB (MATLAB and Statistic Toolbox Release, 2012b), using an in-house developed preprocessing pipeline and the parameters
established in the optimization procedure. Functional data was corrected for motion (using FSL MCFLIRT; Jenkinson, Bannister, Brady, & Smith, 2002) and slice timing and was spatially smoothed (5 mm isotropic kernel). After preprocessing, individual time series were modeled using a double gamma hemodynamic response function convolved with regressors containing the stimulus-onsets using FSL’s FEAT toolbox (FMRIB’s Software Library,
www.fmrib.ox.ac.uk/fsl). Thereafter, a mask of the voxels that reacted more to emotional that control stimuli was created (Z-value cutoff = 2.3). The average values inside the mask were used for statistical testing.
Personality Measure
Neuroticism scores were obtained by the neuroticism scale of the NEO-Five Factory Inventory (NEO-FFI, Dutch version, Hoekstra, Ormel, de Fruyt, 2007), a shorter version of the Revised NEO Personality Inventory (NEO-PI-R). The instrument comprises 60 items, 12 for each personality domain, scoring on a 5-point Likert scale ranging from ‘Totally Disagree’ (Dutch version: ‘Helemaal Oneens’) to ‘Totally Agree’ (Dutch version: ‘Helemaal Eens’). The score range between 60 and 300. Item example: ‘Ik ben geen tobber’. The NEO-FFI is one of the most widely used instruments to measure the five basic personality domains. The Dutch version
of the NEO-FFI has shown satisfactory psychometric proprieties. Based on six studies, the internal consistency for all domains turned out to be acceptable to good. In particular,
neuroticism showed the highest internal consistency with a coefficient ranging from .80 to .88. The stability coefficients (test-retest correlations) are also high. In two studies, neuroticism showed a test-retest correlation of .80 and .82. A Principal Component Analysis produced a clear neuroticism factor. Construct validity is shown by the relation of the NEO-FFI scales with similar scales from other instruments. For instance, the neuroticism-scale of the NEO-FFI correlates with the neuroticism-scale of the Four-Dimensional Personality Test (4DPT; Van Kampen, 1997) and with other constructs, such as aggression, reactive depression, social isolation and anxiety (Hoekstra, Ormel, de Fruyt, 2007).
Data analytic strategy
Firstly, we will determine the regions of interests (ROIs) that appear to be associated with the processing of negative emotional faces by calculating the difference in neural activation between the experimental task (matching negative faces) and the control task (matching scrambled faces). This will provide the most significantly activated voxels in the experimental task compared with the control task. Given that the task is quite short, it won’t be possible to separate anger from fear. Thereafter, correlation analyses will be performed to examine whether individual differences in neural activation in the selected ROIs are correlated with neuroticism scores. The correlation analyses will be performed using SPSS software (version 22).
In order to answer the second research question, whether neuroticism is associated with a behavioral advantage in processing negative emotional faces, four multiple regression analyses will be performed with average reaction time for angry faces, average reaction time for fearful faces, average accuracy for angry faces and average accuracy for fearful faces as dependent variables. The predictors are: average reaction time in the control task, average accuracy in the control task and neuroticism (N). With the four multiple regression analyses we will investigate
whether neuroticism is a significant predictor of reaction time and/or accuracy in matching negative emotional faces, after controlling for reaction time and accuracy in the control condition. The regression analyses will be performed using SPSS software (version 22). If neuroticism is found to be a significant predictor of reaction time in matching angry and fearful faces, a mediation analysis will be performed in order to investigate whether the relation between neuroticism and reaction time is mediated by the activation in the selected ROI’s. This means that different levels of neuroticism lead to the activation of certain brain regions, which in turn are associated with different reaction times in matching the emotional faces. To perform the mediation analysis, we will use the PROCESS macro for SPSS developed by Andrew F. Hayes (http://www.processmacro.org).
RESULTATEN Personality measures
The scores on neuroticism ranged from 13 to 58 (M = 30.72, SD = 7.49). A visual inspection of
the histogram showed that the distribution of the neuroticism scores was approximately normal. Female participants reported higher levels of neuroticism (M = 32.22, SD = 7.32) than male
participants (M = 29.00, SD = 7.51). This difference was significant, t (207) = -3.12, p < .05.
Brain areas that correlate with Neuroticism
A difference in activation between matching emotional faces and matching scrambled faces was found in several brain areas. As expected, the amygdala was stronger activated during the
experimental condition than during the control condition. In order to determine which activated brain areas were significantly correlated with neuroticism scores during the face-matching task, we split the sample into two groups with approximately the same number of participants, respectively 108 and 109. For the first group, we made a mask of voxels that significantly correlated with neuroticism scores and four clusters were found. The first cluster was located in
the left cerebral cortex and included the occipital fusiform gyrus and the inferior temporal gyrus (see Fig. 2). The second cluster was located in the right cerebral cortex and included the
inferior/middle frontal gyrus and the precentral gyrus (see Fig. 3). The third cluster was located in the lateral occipital cortex (see Fig. 4). Finally, the fourth cluster was located in the right cerebral cortex and included the middle/superior frontal gyrus (see Fig. 5). These areas are consistent with those found in previous research (Ormel et al., 2013 and Chan et al., 2009).
Fig 2. Cluster 1: Neuroticism correlated with the occipital fusiform gyrus and the inferior temporal gyrus during the face-matching task.
Fig 3. Cluster 2: Neuroticism correlated with the inferior/middle frontal gyrus and the precentral gyrus during the face-matching task.
Fig. 4. Cluster 3: Neuroticism correlated with the lateral occipital cortex during the face-matching task.
Fig. 5. Cluster 4: Neuroticism correlated with the middle/superior frontal gyrus during the face-matching task.
We tried to cross-validate these results by calculating the correlation between neuroticism and the average value of each participant in the second group for the four significant clusters. For none of the clusters a significant correlation was found (see Table 1).
Table 1
Correlation between neuroticism and brain activation in the four significant clusters
Cluster r p
1 - 0.138 0.152
2 0.127 0.187
4 0.019 0.844
Note. N = 108
Behavioral results
Participants were faster in matching scrambled faces than in matching angry faces or fearful faces (see Table 2).
Table 2
Speed in face-matching task (in milliseconds)
Face M SD Range
Scrambled 1034.07 300.04 404.12 - 2982.53 Angry 1834.00 474.49 404.12 - 2982.53 Fearful 1663.33 437.76 443.73 - 3014.51
Note. N = 211.
We performed a repeated-measure ANOVA in order to examine whether these differences in performance across conditions were significant. Mauchly’s test indicated that the assumption of sphericity has been violated, X2(2) = 0.45, p < .001. Given the violation, degrees of freedom were
corrected using Greenhouse-Geisser estimates of sphericity (ε = .65). The results show that the matching speed was significantly affected by the type of face, F (1.29, 279.54) = 362.93, p < .001, r = 0.77. Bonferroni post-hoc comparisons show that participants were significantly faster in
matching fearful than angry faces. We then performed two hierarchical regression analyses to determine whether neuroticism was a significant predictor of reaction time for matching angry faces and for matching fearful faces. We entered reaction time in the control condition as first predictor and neuroticism as second predictor. This way we could test the hypothesis that neuroticism significantly improves the model with only reaction time in the control condition. For reaction time in matching angry faces, we found that the model with reaction time in the
control condition as unique predictor was significant. Adding neuroticism as second predictor did not significantly improve the model (see Table 3).
Table 3
Regression analysis for reaction time in matching angry faces
B SE B β Step 1 Constant 1353.67 74.26 Reaction time control condition 0.46 0.06 0.45* Step 2 Constant 1222.04 149.04 Reaction time control condition 0.46 0.06 0.45* Neuroticism 4.21 4.13 0.06 Note: R2 = .20 for Step 1, ∆R2 = .004 for step 2 (p = .31). * p < .001
The same result was found for reaction time in matching fearful faces. The model with reaction time in the control condition as unique predictor was found significant and adding neuroticism as second predictor did not improve the model (see Table 4).
Table 4
Regression analysis for reaction time in matching fearful faces
B SE B β
Step 1
Constant 1164.14 67.46 Reaction time
Step 2
Constant 1029.80 135.31 Reaction time
control condition 0.48 0.06 0.50* Neuroticism 4.29 3.75 0.07 Note: R2 = .25 for Step 1, ∆R2 = .005 for step 2 (p = .25). * p < .001
As expected, partecipants were very accurate in performing this task (see Table 5). Table 5
Accuracy in face-matching task
Face M SD Range Scrambled 90.61 1.01 0.00 – 1.00 Angry 90.20 0.87 0.33 – 1.00 Fearful 93.97 0.86 0.25 – 1.00 Note. N = 211.
Participants were more accurate in matching fearful faces than angry faces and scrambled faces. We performed a repeated-measure ANOVA in order to examine whether these differences in performance across conditions were significant. Mauchly’s test indicated that the assumption of sphericity has been violated, X2(2) = 0.85, p < .001. Given the violation, degrees of freedom were
corrected using Greenhouse-Geisser estimates of sphericity (ε = .87). The results show that accuracy was significantly affected by the type of face, F (1.74, 375.46) = 12.19, p < .001, r = 0.24 . Bonferroni post-hoc comparisons show that participants were significantly more accurate in
matching fearful than angry faces.
As for reaction time, two hierarchical regression analyses were performed in order to investigate whether neuroticism is a significant predictor of accuracy in matching negative emotional faces. Accuracy in the control condition was entered as first variable, followed by neuroticism. Adding
neuroticism in the model did not substantially improve prediction of both accuracy in matching angry faces (see Table 6) and accuracy in matching fearful faces (see Table 7).
Table 6
Regression analysis for accuracy in matching angry faces
B SE B β Step 1 Constant 0.47 0.04 Reaction time control condition 0.47 0.05 0.55* Step 2 Constant 0.49 0.05 Reaction time control condition 0.47 0.05 0.55* Neuroticism - 0.001 0.001 - 0.04 Note: R2 = .30 for Step 1, ∆R2 = .002 for step 2 (p = .45). * p < .001
Table 7
Regression analysis for accuracy in matching fearful faces
B SE B β Step 1 Constant 0.54 0.04 Reaction time control condition 0.45 0.05 0.52* Step 2 Constant 0.56 0.05 Reaction time control condition 0.45 0.05 0.53* Neuroticism - 0.001 0.001 - 0.06 Note: R2 = .27 for Step 1, ∆R2 = .004 for step 2 (p = .28). * p < .001
Discussion
This study aimed to investigate whether neuroticism moderates the activation of brain regions associated with processing of fearful and angry faces. Four clusters in the brain were found to be positively correlated with neuroticism: 1) Left occipital fusiform gyrus and inferior temporal gyrus; 2) Right inferior/middle frontal gyrus and precentral gyrus; 3) Left lateral occipital cortex; 4) Right middle/superior frontal gyrus. These findings are consistent with those of Chan et al. (2009), in which participants with high neuroticism scores showed stronger activation in the right fusiform gyrus and left middle temporal gyrus when exposed to facial expression of fear than participants with low neuroticism scores. Moreover, Chan et al. (2009) found that high-N participants demonstrated a more heightened response in the left middle frontal gyrus to fearful v. happy faces with medium intensity than low-N participants. We also found a correlation between left middle frontal gyrus and neuroticism during the exposure to fearful and angry faces. Canli et al. (2001) also found a positive correlation between neuroticism and left temporal and frontal gyri when participants viewed pictures with negative valence relative to picture with positive valence. Interestingly, we found a significant difference in amygdala activation between the experimental condition and the control condition, but this difference did not significantly correlate with neuroticism. This means that exposure to faces displaying negative emotions leads to an increased amygdala activation regardless the level of neuroticism. These results suggest that certain brain areas are particularly involved in the processing of faces displaying negative
emotions and that neuroticism might play a role by strengthening the activation of some but not all of these areas in highly neurotic individuals. However, the correlations that we found were not significant. One possible explanation for the lack of significance is the type of sample employed in this study. Our sample consisted of healthy young students of age between 18 and 26 years. This population is quite specific and this might have influenced our results. A sample consisting of participants with a broader age range and comprising also individuals with psychological
pathology might have generated a different result. An alternative explanation is that the correlation between neuroticism and activation of certain ROIs is state dependent (level of stress), as some research has suggested. For instance, Eveready, Klumpers, van Wingen, Tedolkar, and Fernández (2015) found that neuroticism was associated with a heightened
amygdala response to emotional faces but that this association depended on whether participants were in a stressful condition or not.
Besides investigating the neural response to negative emotional faces, in this study we also considered the behavioral response of participants by calculating reaction time and accuracy in matching the stimuli. We found that neuroticism was not a significant predictor of either reaction time or accuracy. This means that different levels of neuroticism did not influence how fast and accurate participants matched the emotional faces. This result doesn’t support the cognitive theory of Mathews and MacLeod (1994, 2005), according to which anxiety-prone individuals have a bias toward emotionally negative cues. There might be two explanations for this finding. It might be that the bias processing of neurotic individuals only concerns positive facial emotions, in accordance with previous results (Andric et al., 2010; Chan, Goodwin, & Harmer, 2007). In this task there are no happy faces, not allowing this hypothesis to be tested. Another possible explanation is that individuals scoring high on neuroticism have difficulty to disengage their attention from the threatening target face, as some studies have pointed out (Fox, Russo, Bowles, & Dutton, 2001; Fox, Russo, & Dutton, 2002; Yiend & Mathews, 2001). If this is the case, high neurotic individuals won’t be faster in matching the emotional faces than low neurotic individuals and therefore, no behavioral advantage will be shown.
Interestingly, several neuroimaging studies on personality have shown increasing or diminishing in brain activity without the corresponding differences in the behavioral task performance. One possible explanation for this phenomenon is provided by the concept of neural efficiency. Some
individuals, such as high extravert individuals, might be able to perform a task equally well as others, such as neurotic individuals, given reduced brain activity (Yarkoni, 2013). Bistriky,
Ingram, and Atchley (2011) suggest different explanations for differences in neural activity in the absence of behavioral differences: 1) Measures of behavioral activity might be more sensitive than behavioral measures; 2) Changes in neural activity might precede further differences in performance; 3) Different brain areas might be involved in order to maintain a normal task performance. Canli et al. (2001) also claim that the correlations between brain activity and personality traits may be stronger compared to behavioral data. They suggest that the strong correlation between activation of individual brain structures and personality constructs might be obscured at the performance level, given that behavior is the result of the sum of several active brain areas. In the current study, we found that certain brain areas correlated with neuroticism during processing of fearful and angry faces but that these correlations were not significant. Given that the differences in brain activation between individuals with different levels of neuroticism were not strong, it is not surprising that no behavioral effect was found. An interesting finding is that participants were significantly faster and more accurate in matching fearful faces than angry faces, regardless of their level of neuroticism. There has been growing evidence of dissociable neural systems for the processing of angry faces as compared to faces displaying fear. Pichon, de Gelder, & Grèzes (2009) found that certain brain areas are involved in both perception of fear and anger. At the same time, there are several brain regions specifically activated as a reaction to perception of anger. The authors suggest that individuals exposed to anger as compared to fear need to deduce more information from the environment in order to implement adaptive behavior. This distinct neural reaction to fearful v. angry faces is already evident during infancy. One study employing event-related potentials (ERPs) found that 7-month old infants allocated more attentional resources to angry than fearful faces, measured by a larger amplitude of negative component (Nc) in fronto-central brain areas (Kobiella,
Grossmann, Reid, & Striano, 2008). In another study, participants showed a different startle reaction when exposed to fear as compared to anger. Specifically, they showed a significantly stronger startle reflex when perceiving anger than when perceiving fear. There is thus a
dissociable psychophysiological response to perception of fear vs perception of anger (Springer, Rosas, McGetrick, & Bowers, 2007). All these studies show that even though fearful and angry faces are both considered threatening stimuli, individuals perceive and react to them in a different way. In our study, participants were significantly less quick and less accurate in matching angry than fearful faces. This might be explained by assuming that anger is a more direct threat to the viewer than fear. As Springer et al. (2007) suggest, when individuals see an angry face, they have the tendency to withdraw in order to protect themselves from an imminent threat. This can explain why participants in the current study were slower in matching angry faces than in matching fearful faces.
In summary, we found that activation of certain brain areas during the face-matching task was correlated with neuroticism, suggesting that individual differences in neuroticism might affect neural response. However, these correlations were not significant. This result can be explained by the specificity of the sample applied in this study or by assuming, as suggested by previous research, that these correlations are state dependent. Moreover, we didn’t find any behavioral effect of neuroticism but it appeared that participants were significantly less accurate and less quick in matching angry v. fearful faces, regardless of their neuroticism level. This result adds to the growing body of evidence showing that fear and anger are two different types of threat to which individuals respond in a distinct way.
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